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speech Recognition via CTC-CNN Model

Computers, Materials & Continua 2023年第9期76卷 3833-3858页

作者： Wen-Tsai Sung Hao-WeiKang Sung-Jung Hsiao Department of Electrical Engineering National Chin-Yi University of TechnologyTaichung411030Taiwan Department of Information Technology Takming University of Science and TechnologyTaipei11451Taiwan

In the speech recognition system,the acoustic model is an important underlying model,and its accuracy directly affects the performance of the entire *** paper introduces the construction and training process of the acoustic model in detail and studies the Connectionist temporal classification(CTC)algorithm,which plays an important role in the end-to-end framework,established a convolutional neural network(CNN)combined with an acoustic model of Connectionist temporal classification to improve the accuracy of speech *** study uses a sound sensor,ReSpeakerMic Array v2.0.1,to convert the collected speech signals into text or corresponding speech signals to improve communication and reduce noise and hardware *** baseline acousticmodel in this study faces challenges such as long training time,high error rate,and a certain degree of *** model is trained through continuous design and improvement of the relevant parameters of the acousticmodel,and finally the performance is selected according to the evaluation ***,which reduces the error rate to about 18%,thus improving the accuracy ***,comparative verificationwas carried out from the selection of acoustic feature parameters,the selection of modeling units,and the speaker’s speech rate,which further verified the excellent performance of the CTCCNN_5+BN+Residual model *** terms of experiments,to train and verify the CTC-CNN baseline acoustic model,this study uses THCHS-30 and ST-CMDS speech data sets as training data sets,and after 54 epochs of training,the word error rate of the acoustic model training set is 31%,the word error rate of the test set is stable at about 43%.This experiment also considers the surrounding environmental *** the noise level of 80∼90 dB,the accuracy rate is 88.18%,which is the worst performance among all *** contrast,at 40–60 dB,the accuracy was as high as 97.33%due to less noise pollution.

关键词： Artificial intelligence speech recognition speech to text convolutional neural network automatic speech recognition

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Improving speech Enhancement Framework via Deep Learning

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Computers, Materials & Continua 2023年第5期75卷 3817-3832页

作者： Sung-Jung Hsiao Wen-Tsai Sung Department of Information Technology Takming University of Science and TechnologyTaipei City11451Taiwan Department of Electrical Engineering National Chin-Yi University of TechnologyTaichung411030Taiwan

speech plays an extremely important role in social *** individuals suffer from a“speech barrier,”which limits their communication with *** this study,an improved speech recognitionmethod is proposed that addresses the needs of speech-impaired and deaf individuals.A basic improved connectionist temporal classification convolutional neural network(CTC-CNN)architecture acoustic model was constructed by combining a speech database with a deep neural *** sensors were used to convert the collected voice signals into text or corresponding voice signals to improve *** method can be extended to modern artificial intelligence techniques,with multiple applications such as meeting minutes,medical reports,and verbatim records for cars,sales,*** experiments,a modified CTC-CNN was used to train an acoustic model,which showed better performance than the earlier common *** a CTC-CNN baseline acoustic model was constructed and optimized,which reduced the error rate to about 18%and improved the accuracy rate.

关键词： Artificial intelligence speech recognition speech to text CTC-CNN

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A COMPLEMENTARY APPROACH TO COMPUTER-AIDED TRANSCRIPTION: SYNERGY OF STATISTICAL-BASED AND KNOWLEDGE DISCOVERY PARADIGMS

A COMPLEMENTARY APPROACH TO COMPUTER-AIDED TRANSCRIPTION: SY...

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6～(th) International Conference on Spoken Language Processing

作者： Benjamin K.T’sou Tom B.Y.Lai Language Information Sciences Research Centre City University of Hong Kong Tat Chee Avenue Kowloon Tong Hong Kong SAR China Language Information Sciences Research Centre City University of Hong Kong Tat Chee Avenue Kowloon Tong Hong Kong SAR China

＜正＞ The recent implementation of legal bilingualism necessitates the development of a Chinese Computer-Aided Transcription （CAT） system to product Chinese court proceedings conducted in Cantonese. The transcription system converts transcription shorthand codes into Chinese text, i.e., from phonetic to textual representation of the language. Cantonese and Mandarin Chinese have many homophonous characters. The main challenge lies in the resolution of the severe ambiguity of the conversion. N-gram statistical model is incorporated to estimate the most probable character string during conversion. Domain- specific corpora have been compiled to support the statistical computation. With additional enhancement features, the CAT system delivers a transcription accuracy of 96%. An intelligent error detection tool is built into the system to facilitate the manual correction of the remaining errors. Using decision tree algorithm and a range of text and linguistic attributes, the system can effectively alert the users to possible errors.

关键词： speech to text Statistical Modelling Cantonese Chinese

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